diff --git a/app/service/generate_image/utils/image_processing.py b/app/service/generate_image/utils/image_processing.py
index 2883129..3602cab 100644
--- a/app/service/generate_image/utils/image_processing.py
+++ b/app/service/generate_image/utils/image_processing.py
@@ -1,4 +1,6 @@
 import logging
+import time
+
 import mmcv
 import numpy as np
 import torch
@@ -15,7 +17,7 @@ logger = logging.getLogger()
 def seg_preprocess(img_path):
     img = mmcv.imread(img_path)
     ori_shape = img.shape[:2]
-    img_scale = (224, 224)
+    img_scale = ori_shape
     scale_factor = []
     img, x, y = mmcv.imresize(img, img_scale, return_scale=True)
     scale_factor.append(x)
@@ -61,6 +63,26 @@ def get_contours(image):
     return Contour
 
 
+# def seg_infer_image(image_obj):
+#     image, ori_shape = seg_preprocess(image_obj)
+#     client = httpclient.InferenceServerClient(url=f"{SEG_MODEL_URL}")
+#     transformed_img = image.astype(np.float32)
+#     # 输入集
+#     inputs = [
+#         httpclient.InferInput(SEGMENTATION['input'], transformed_img.shape, datatype="FP32")
+#     ]
+#     inputs[0].set_data_from_numpy(transformed_img, binary_data=True)
+#     # 输出集
+#     outputs = [
+#         httpclient.InferRequestedOutput(SEGMENTATION['output'], binary_data=True),
+#     ]
+#     results = client.infer(model_name=SEGMENTATION['name'], inputs=inputs, outputs=outputs)
+#     # 推理
+#     # 取结果
+#     inference_output1 = torch.from_numpy(results.as_numpy(SEGMENTATION['output']))
+#     seg_result = seg_postprocess(inference_output1, ori_shape)
+#     return seg_result
+
 def seg_infer_image(image_obj):
     image, ori_shape = seg_preprocess(image_obj)
     client = httpclient.InferenceServerClient(url=f"{SEG_MODEL_URL}")
@@ -74,21 +96,29 @@ def seg_infer_image(image_obj):
     outputs = [
         httpclient.InferRequestedOutput(SEGMENTATION['output'], binary_data=True),
     ]
-    results = client.infer(model_name=SEGMENTATION['name'], inputs=inputs, outputs=outputs)
+    start_time = time.time()
+    results = client.infer(model_name=SEGMENTATION['new_model_name'], inputs=inputs, outputs=outputs)
+    print(f"KNet infer time is :{time.time() - start_time}")
     # 推理
     # 取结果
-    inference_output1 = torch.from_numpy(results.as_numpy(SEGMENTATION['output']))
+    inference_output1 = results.as_numpy(SEGMENTATION['output'])
     seg_result = seg_postprocess(inference_output1, ori_shape)
     return seg_result
 
+# def seg_postprocess(output, ori_shape):
+#     seg_logit = F.interpolate(output, size=ori_shape, scale_factor=None, mode='bilinear', align_corners=False)
+#     seg_logit = F.softmax(seg_logit, dim=1)
+#     seg_pred = seg_logit.argmax(dim=1)
+#     seg_pred = seg_pred.cpu().numpy()
+#     return seg_pred
 
+# KNet
 def seg_postprocess(output, ori_shape):
-    seg_logit = F.interpolate(output, size=ori_shape, scale_factor=None, mode='bilinear', align_corners=False)
-    seg_logit = F.softmax(seg_logit, dim=1)
-    seg_pred = seg_logit.argmax(dim=1)
-    seg_pred = seg_pred.cpu().numpy()
-    return seg_pred
-
+    # seg_logit = F.interpolate(torch.tensor(output).float(), size=ori_shape, scale_factor=None, mode='bilinear', align_corners=False)
+    # seg_logit = F.softmax(seg_logit, dim=1)
+    # seg_pred = seg_logit.argmax(dim=1)
+    # seg_pred = output.cpu().numpy()
+    return output[0]
 
 def remove_background(image):
     image_obj, mask = get_mask(image)